
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


SCREAMING_SNAKE_CASE :		List[str]       			=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :		List[Any]       			=							OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

SCREAMING_SNAKE_CASE :		Optional[Any]       			=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 							lowerCamelCase_	( __UpperCamelCase  ):





				for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
								if class_name in extractors:
												A_													=	model_type_to_module_name(A_  )

												A_													=	importlib.import_module(F".{module_name}"			,    '''transformers.models'''  )
												try:
																return getattr(A_			,    A_  )
												except AttributeError:
																continue

				for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
								if getattr(A_			,    '''__name__'''			,    A_  ) == class_name:
												return extractor

								# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
								# init and we return the proper dummy to get an appropriate error message.
				A_													=	importlib.import_module('''transformers'''  )
				if hasattr(A_			,    A_  ):
								return getattr(A_			,    A_  )

				return None



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase = None			,    __UpperCamelCase = False			,    __UpperCamelCase = False			,    __UpperCamelCase = None			,    __UpperCamelCase = None			,    __UpperCamelCase = None			,    __UpperCamelCase = False			,    **__UpperCamelCase			,    ):
				A_													=	get_file_from_repo(
				    A_			,    A_			,    cache_dir=A_			,    force_download=A_			,    resume_download=A_			,    proxies=A_			,    use_auth_token=A_			,    revision=A_			,    local_files_only=A_			,    )
				if resolved_config_file is None:
								logger.info(
								    '''Could not locate the feature extractor configuration file, will try to use the model config instead.'''  )
								return {}

				with open(A_			,    encoding='''utf-8'''  ) as reader:
								return json.load(A_  )






class        __lowercase		:


					def __init__(						self    )		->				Optional[int]:





									'''simple docstring'''
									raise EnvironmentError(
									    '''AutoFeatureExtractor is designed to be instantiated '''
									    '''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'''    )


					@classmethod
					@replace_list_option_in_docstrings(UpperCamelCase__    )
					def 							lowerCAmelCase_     (						cls							,							a__							,							**a__    )		->				str:





									'''simple docstring'''
									A_													=	kwargs.pop('''config'''							,							UpperCamelCase__    )
									A_													=	kwargs.pop('''trust_remote_code'''							,							UpperCamelCase__    )
									A_													=	True

									A_						,       A_													=	FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase__							,							**UpperCamelCase__    )
									A_													=	config_dict.get('''feature_extractor_type'''							,							UpperCamelCase__    )
									A_													=	None
									if "AutoFeatureExtractor" in config_dict.get('''auto_map'''							,							{}    ):
													A_													=	config_dict['''auto_map''']['''AutoFeatureExtractor''']

									# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
									if feature_extractor_class is None and feature_extractor_auto_map is None:
													if not isinstance(UpperCamelCase__							,							UpperCamelCase__    ):
																	A_													=	AutoConfig.from_pretrained(UpperCamelCase__							,							**UpperCamelCase__    )
													# It could be in `config.feature_extractor_type``
													A_													=	getattr(UpperCamelCase__							,							'''feature_extractor_type'''							,							UpperCamelCase__    )
													if hasattr(UpperCamelCase__							,							'''auto_map'''    ) and "AutoFeatureExtractor" in config.auto_map:
																	A_													=	config.auto_map['''AutoFeatureExtractor''']

									if feature_extractor_class is not None:
													A_													=	feature_extractor_class_from_name(UpperCamelCase__    )

									A_													=	feature_extractor_auto_map is not None
									A_													=	feature_extractor_class is not None or type(UpperCamelCase__    ) in FEATURE_EXTRACTOR_MAPPING
									A_													=	resolve_trust_remote_code(
									    UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__    )

									if has_remote_code and trust_remote_code:
													A_													=	get_class_from_dynamic_module(
													    UpperCamelCase__							,							UpperCamelCase__							,							**UpperCamelCase__    )
													A_													=	kwargs.pop('''code_revision'''							,							UpperCamelCase__    )
													if os.path.isdir(UpperCamelCase__    ):
																	feature_extractor_class.register_for_auto_class()
													return feature_extractor_class.from_dict(UpperCamelCase__							,							**UpperCamelCase__    )
									elif feature_extractor_class is not None:
													return feature_extractor_class.from_dict(UpperCamelCase__							,							**UpperCamelCase__    )
									# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
									elif type(UpperCamelCase__    ) in FEATURE_EXTRACTOR_MAPPING:
													A_													=	FEATURE_EXTRACTOR_MAPPING[type(UpperCamelCase__    )]
													return feature_extractor_class.from_dict(UpperCamelCase__							,							**UpperCamelCase__    )

									raise ValueError(
									    F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
									    F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
									    F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()    )}"    )


					@staticmethod
					def 							lowerCAmelCase_     (						a__							,							a__    )		->				Dict:





									'''simple docstring'''
									FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase__							,							UpperCamelCase__    )

import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowerCAmelCase     :	Dict       			=  'src/transformers'
__lowerCAmelCase     :	List[Any]       			=  'docs/source/en'
__lowerCAmelCase     :	str       			=  '.'

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  with open(A_,	"""r""",	encoding="""utf-8""",	newline="""\n"""   ) as f:
    __magic_name__								=			f.readlines()
  # Find the start prompt.
  __magic_name__								=			0
  while not lines[start_index].startswith(A_   ):
    start_index += 1
  start_index += 1

  __magic_name__								=			start_index
  while not lines[end_index].startswith(A_   ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]   ) <= 1:
    start_index += 1
  while len(lines[end_index]   ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__lowerCAmelCase     :	str       			=  'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase     :	List[str]       			=  re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__lowerCAmelCase     :	Any       			=  re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCAmelCase     :	Optional[int]       			=  re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase     :	Union[str, Any]       			=  direct_transformers_import(TRANSFORMERS_PATH)

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""",	A_   )
  return [m.group(0   ) for m in matches]

def a__       (			A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			2 if text == """✅""" or text == """❌""" else len(A_   )
  __magic_name__								=			(width - text_length) // 2
  __magic_name__								=			width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  __magic_name__								=			{
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  __magic_name__								=			{name: config.replace("""Config""",	""""""   ) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )

  # Let's lookup through all transformers object (once).
  for attr_name in dir(A_   ):
    __magic_name__								=			None
    if attr_name.endswith("""Tokenizer"""   ):
      __magic_name__								=			slow_tokenizers
      __magic_name__								=			attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""   ):
      __magic_name__								=			fast_tokenizers
      __magic_name__								=			attr_name[:-13]
    elif _re_tf_models.match(A_   ) is not None:
      __magic_name__								=			tf_models
      __magic_name__								=			_re_tf_models.match(A_   ).groups()[0]
    elif _re_flax_models.match(A_   ) is not None:
      __magic_name__								=			flax_models
      __magic_name__								=			_re_flax_models.match(A_   ).groups()[0]
    elif _re_pt_models.match(A_   ) is not None:
      __magic_name__								=			pt_models
      __magic_name__								=			_re_pt_models.match(A_   ).groups()[0]

    if lookup_dict is not None:
      while len(A_   ) > 0:
        if attr_name in model_name_to_prefix.values():
          __magic_name__								=			True
          break
        # Try again after removing the last word in the name
        __magic_name__								=			"""""".join(camel_case_split(A_   )[:-1]   )

    # Let's build that table!
  __magic_name__								=			list(model_name_to_config.keys()   )
  model_names.sort(key=str.lower   )
  __magic_name__								=			["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  __magic_name__								=			[len(A_   ) + 2 for c in columns]
  __magic_name__								=			max([len(A_   ) for name in model_names]   ) + 2

  # Build the table per se
  __magic_name__								=			"""|""" + """|""".join([_center_text(A_,	A_   ) for c, w in zip(A_,	A_   )]   ) + """|\n"""
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]   ) + "|\n"

  __magic_name__								=			{True: """✅""", False: """❌"""}
  for name in model_names:
    __magic_name__								=			model_name_to_prefix[name]
    __magic_name__								=			[
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(A_,	A_   ) for l, w in zip(A_,	A_   )]   ) + "|\n"
  return table

def a__       (			A_=False   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			_find_text_in_file(
      filename=os.path.join(A_,	"""index.md"""   ),	start_prompt="""<!--This table is updated automatically from the auto modules""",	end_prompt="""<!-- End table-->""",	)
  __magic_name__								=			get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(A_,	"""index.md"""   ),	"""w""",	encoding="""utf-8""",	newline="""\n"""   ) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]   )
    else:
      raise ValueError(
          """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Tuple       			=  argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    __lowerCAmelCase     :	Optional[Any]       			=  parser.parse_args()

    check_model_table(args.fix_and_overwrite)

